1635844372
Aprenda a usar la biblioteca de transformadores Huggingface para generar respuestas conversacionales con el modelo DialoGPT previamente entrenado en Python.
Los chatbots han ganado mucha popularidad en los últimos años y, a medida que crece el interés en el uso de chatbots para empresas, los investigadores también hicieron un gran trabajo en el avance de los chatbots de IA conversacionales.
En este tutorial, usaremos la biblioteca de transformadores Huggingface para emplear el modelo DialoGPT previamente entrenado para la generación de respuestas conversacionales.
DialoGPT es un modelo de generación de respuesta conversacional neuronal sintonizable a gran escala que se entrenó en 147 millones de conversaciones extraídas de Reddit, y lo bueno es que puede ajustarlo con su conjunto de datos para lograr un mejor rendimiento que el entrenamiento desde cero.
Para comenzar, instalemos transformadores :
$ pip3 install transformers
Abra un nuevo archivo o cuaderno de Python y haga lo siguiente:
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
# model_name = "microsoft/DialoGPT-large"
model_name = "microsoft/DialoGPT-medium"
# model_name = "microsoft/DialoGPT-small"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)
Hay tres versiones de DialoGPT; pequeño, mediano y grande. Por supuesto, cuanto más grande, mejor, pero si ejecuta esto en su máquina, creo que el tamaño pequeño o mediano se adapta a su memoria sin problemas. También puede utilizar Google Colab para probar el más grande.
En esta sección, usaremos el algoritmo de búsqueda codiciosa para generar respuestas. Es decir, seleccionamos la respuesta del chatbot que tiene la mayor probabilidad de ser seleccionada en cada paso de tiempo.
Hagamos un código para chatear con nuestra IA usando una búsqueda codiciosa:
# chatting 5 times with greedy search
for step in range(5):
# take user input
text = input(">> You:")
# encode the input and add end of string token
input_ids = tokenizer.encode(text + tokenizer.eos_token, return_tensors="pt")
# concatenate new user input with chat history (if there is)
bot_input_ids = torch.cat([chat_history_ids, input_ids], dim=-1) if step > 0 else input_ids
# generate a bot response
chat_history_ids = model.generate(
bot_input_ids,
max_length=1000,
pad_token_id=tokenizer.eos_token_id,
)
#print the output
output = tokenizer.decode(chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True)
print(f"DialoGPT: {output}")
Expliquemos el núcleo de este código:
input_ids
usar el tokenizador DialoGPT, también agregamos el final del token de cadena y lo devolvemos como un tensor de Pytorch.input_ids
nuestro modelo durante una generación. De lo contrario, agregamos el historial de chat usando la concatenación con la ayuda del torch.cat()
método.model.generate()
método para generar la respuesta del chatbot.tokenizer.decode()
y configuramos skip_special_tokens
para True
asegurarnos de que no veamos ningún token especial molesto como <|endoftext|>
. Además, dado que el modelo devuelve la secuencia completa, omitimos el historial de chat anterior e imprimimos solo la respuesta del chatbot recién generada.A continuación se muestra una discusión de muestra con el bot:
>> You:How can you be rich so quickly?
DialoGPT: I'm not rich, I'm just a rich man.
>> You:Ok, you don't make sense, how old are you ?
DialoGPT: I'm not a rich man, I'm just a rich man.
>> You:How can you be happy ?
DialoGPT: I'm not happy, I'm just a rich man.
>> You:But they say money buys happiness!
DialoGPT: Money buys happiness, but not happiness itself.
>> You:You don't make sense again!
DialoGPT: I'm not happy, I'm just a rich man.
Verá que el modelo repite muchas respuestas, ya que estas son la probabilidad más alta y lo elige cada vez.
De forma predeterminada, model.generate()
utiliza un algoritmo de búsqueda codicioso cuando no se establecen otros parámetros; en las siguientes secciones, agregaremos algunos argumentos a este método para ver si podemos mejorar la generación.
La búsqueda por haz nos permite reducir el riesgo de perder secuencias de alta probabilidad al mantener las num_beams
hipótesis más probables en cada paso de tiempo y luego tomar las secuencias que tienen la probabilidad general más alta, el siguiente código generará respuestas de chatbot con búsqueda de haz:
# chatting 5 times with beam search
for step in range(5):
# take user input
text = input(">> You:")
# encode the input and add end of string token
input_ids = tokenizer.encode(text + tokenizer.eos_token, return_tensors="pt")
# concatenate new user input with chat history (if there is)
bot_input_ids = torch.cat([chat_history_ids, input_ids], dim=-1) if step > 0 else input_ids
# generate a bot response
chat_history_ids = model.generate(
bot_input_ids,
max_length=1000,
num_beams=3,
early_stopping=True,
pad_token_id=tokenizer.eos_token_id
)
#print the output
output = tokenizer.decode(chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True)
print(f"DialoGPT: {output}")
Cuando se ajusta num_beams
a 3
de model.generate()
método, a continuación, vamos a seleccionar 3 palabras en cada paso de tiempo y desarrollarlas para encontrar la más alta probabilidad global de la secuencia, el establecimiento num_beams
de 1 es la misma que la búsqueda codiciosa.
A continuación se muestra una discusión de muestra con el chatbot usando la búsqueda de haz:
>> You:How can I be rich?
DialoGPT: You can't.
>> You:Why not?
DialoGPT: Because you can't.
>> You:Then how am I supposed to live well?
DialoGPT: You can't.
>> You:So basically, you want me to be a poor guy?
DialoGPT: No, I want you to be a rich guy.
>> You:Ok how to be rich, then?
DialoGPT: I don't know.
En las secciones anteriores, usamos la búsqueda de rayos y codiciosos para generar la secuencia de probabilidad más alta. Eso es genial para tareas como la traducción automática o el resumen de texto donde el resultado es predecible. Sin embargo, no es la mejor opción para una generación abierta como en los chatbots.
Para una mejor generación, necesitamos introducir algo de aleatoriedad donde muestreamos de una amplia gama de secuencias candidatas basadas en probabilidades:
# chatting 5 times with sampling
for step in range(5):
# take user input
text = input(">> You:")
# encode the input and add end of string token
input_ids = tokenizer.encode(text + tokenizer.eos_token, return_tensors="pt")
# concatenate new user input with chat history (if there is)
bot_input_ids = torch.cat([chat_history_ids, input_ids], dim=-1) if step > 0 else input_ids
# generate a bot response
chat_history_ids = model.generate(
bot_input_ids,
max_length=1000,
do_sample=True,
top_k=0,
pad_token_id=tokenizer.eos_token_id
)
#print the output
output = tokenizer.decode(chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True)
print(f"DialoGPT: {output}")
Esta vez, establece do_sample
que True
para el muestreo, y nos pusimos top_k
a 0
lo que indica que estamos seleccionando todas las probabilidades posibles, vamos a discutir más adelante top_k
parámetro.
Aquí hay un chat con estos parámetros:
>> You:Hello, how can I be rich?
DialoGPT: You can't
>> You:Really? then how others get rich?
DialoGPT: They work as money artists
>> You:How to be a money artist?
DialoGPT: I don't know, I don't have enough knowledge on artists
>> You:Alright, thank you!
DialoGPT: No problem, if you need tips pm me.
>> You:Great, you're my savior!
DialoGPT: Thank you for helping a stranger out like me!
Claramente hay algunas mejoras. Sin embargo, el muestreo en una amplia lista de secuencias con bajas probabilidades puede conducir a una generación aleatoria (como se ve en la última oración).
Para mejorarlo aún más, podemos:
temperature
Disminuir el muestreo , eso nos ayuda a disminuir la probabilidad de elegir palabras de baja probabilidad y aumentar la probabilidad de elegir palabras de alta probabilidad.# chatting 5 times with Top K sampling & tweaking temperature
for step in range(5):
# take user input
text = input(">> You:")
# encode the input and add end of string token
input_ids = tokenizer.encode(text + tokenizer.eos_token, return_tensors="pt")
# concatenate new user input with chat history (if there is)
bot_input_ids = torch.cat([chat_history_ids, input_ids], dim=-1) if step > 0 else input_ids
# generate a bot response
chat_history_ids = model.generate(
bot_input_ids,
max_length=1000,
do_sample=True,
top_k=100,
temperature=0.75,
pad_token_id=tokenizer.eos_token_id
)
#print the output
output = tokenizer.decode(chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True)
print(f"DialoGPT: {output}")
Ahora, configuramos top_k
para 100
muestrear las 100
palabras principales ordenadas de forma descendente por probabilidad. También establecemos temperature
en 0.75
(el valor predeterminado es 1.0
) para brindar una mayor probabilidad de elegir palabras de alta probabilidad, establecer la temperatura en 0.0
es lo mismo que la búsqueda codiciosa, establecerla en infinito es lo mismo que completamente aleatorio.
Aquí hay una discusión con estos parámetros:
>> You:How can I get rich?
DialoGPT: I'd also like to know this.
>> You:So you don't know it?
DialoGPT: I know it, but it doesn't matter.
>> You:Really? money matters, man!
DialoGPT: Well, money is money and I gotta get my money.
>> You:Yeah how to get them?
DialoGPT: Well, that's a question. I'd like to know more, just for curiosity's sake, to know what other people have said about how I should get rich.
>> You:Some robots like you told me you have to buy gold, silver or stocks.
DialoGPT: I think my wallet is empty.
Como se puede ver, es mucho mejor ahora, no dude en modificar temperature
y top_k
parámetros y ver si puede mejorarlo.
El muestreo de núcleo o muestreo de Top-p elige entre las palabras más pequeñas posibles cuya probabilidad acumulada excede el parámetro p
que establecimos.
A continuación se muestra un ejemplo con el muestreo de Top-p:
# chatting 5 times with nucleus sampling & tweaking temperature
for step in range(5):
# take user input
text = input(">> You:")
# encode the input and add end of string token
input_ids = tokenizer.encode(text + tokenizer.eos_token, return_tensors="pt")
# concatenate new user input with chat history (if there is)
bot_input_ids = torch.cat([chat_history_ids, input_ids], dim=-1) if step > 0 else input_ids
# generate a bot response
chat_history_ids = model.generate(
bot_input_ids,
max_length=1000,
do_sample=True,
top_p=0.95,
top_k=0,
temperature=0.75,
pad_token_id=tokenizer.eos_token_id
)
#print the output
output = tokenizer.decode(chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True)
print(f"DialoGPT: {output}")
Configuramos top_k
para 0
deshabilitar el muestreo de Top-k, pero puede usar ambos métodos, lo que tiende a funcionar mejor. Aquí hay un chat:
>> You:How can I be rich fast?
DialoGPT: Inflation and free money
>> You:lol, but that takes time, I need fast money.
DialoGPT: Your post makes no sense. You just need a lot of money to be rich fast
>> You:Ok how to get a lot of money?
DialoGPT: Buy gold and then trade it for money.
>> You:Hmm, I got your point, thanks.
DialoGPT: No problem. You have to have money to buy gold, but its only good for trading it.
>> You:Yeah, I understand, thank you so much.
DialoGPT: Ok, let me know if you need help
Ahora el chatbot claramente tiene sentido en muchos casos.
Ahora agreguemos algo de código para generar más de una respuesta de chatbot, y luego elegimos qué respuesta incluir en la siguiente entrada:
# chatting 5 times with nucleus & top-k sampling & tweaking temperature & multiple
# sentences
for step in range(5):
# take user input
text = input(">> You:")
# encode the input and add end of string token
input_ids = tokenizer.encode(text + tokenizer.eos_token, return_tensors="pt")
# concatenate new user input with chat history (if there is)
bot_input_ids = torch.cat([chat_history_ids, input_ids], dim=-1) if step > 0 else input_ids
# generate a bot response
chat_history_ids_list = model.generate(
bot_input_ids,
max_length=1000,
do_sample=True,
top_p=0.95,
top_k=50,
temperature=0.75,
num_return_sequences=5,
pad_token_id=tokenizer.eos_token_id
)
#print the outputs
for i in range(len(chat_history_ids_list)):
output = tokenizer.decode(chat_history_ids_list[i][bot_input_ids.shape[-1]:], skip_special_tokens=True)
print(f"DialoGPT {i}: {output}")
choice_index = int(input("Choose the response you want for the next input: "))
chat_history_ids = torch.unsqueeze(chat_history_ids_list[choice_index], dim=0)
He configurado num_return_sequences
para 5
devolver 5 oraciones a la vez, tenemos que elegir la que se incluirá en la siguiente secuencia. Así es como fue:
>> You:Hello, friend.
DialoGPT 0: Hey there!
DialoGPT 1: I am your friend, and I am also your friend.
DialoGPT 2: Hello. :
DialoGPT 3: Hiya! How are you?
DialoGPT 4: Hello. I like your username.
Choose the response you want for the next input: 3
>> You:Fine, thanks, what about you?
DialoGPT 0: Good. Not much to do.
DialoGPT 1: Not much, you?
DialoGPT 2: Fine, thanks.
DialoGPT 3: I'm okay, thank you for asking.
DialoGPT 4: I'm fine too.
Choose the response you want for the next input: 2
>> You:Can you tell me how can I get rich quickly?
DialoGPT 0: Yes, I can!
DialoGPT 1: Yes, just look up how to get rich on Google.
DialoGPT 2: Just keep doing your thing. You'll get rich.
DialoGPT 3: No, I can't.
DialoGPT 4: no, i have no money
Choose the response you want for the next input: 1
>> You:Yeah, but I thought you have an answer...
DialoGPT 0: I'm getting married in the morning.
DialoGPT 1: How do you get rich?
DialoGPT 2: I think there are many good answers, but I could send you one, if you want.
DialoGPT 3: I will, thanks!
DialoGPT 4: I thought you have an answer.
Choose the response you want for the next input: 2
>> You:Yes, please send me.
DialoGPT 0: You can send me link.
DialoGPT 1: I am very interested
DialoGPT 2: I sent you a PM
DialoGPT 3: I'll send you a PM
DialoGPT 4: I am always interested in new ideas.
Choose the response you want for the next input: 2
Y ahí lo tienes, espero que este tutorial te haya ayudado a generar texto en DialoGPT y modelos similares. Para obtener más información sobre cómo generar texto, le recomiendo que lea la guía Cómo generar texto con Transformers .
Te dejo ajustando los parámetros para ver si puedes hacer que el bot funcione mejor.
Además, puede combinar esto con tutoriales de texto a voz y de voz a texto para crear un asistente virtual como Alexa , Siri , Cortana , etc.
#python #chatbot #ai
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